OpenRaiser/Intern-Atlas huggingface dataset:
SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes
Venue: arXiv cs.CV 2015
Year: 2015

We present an open-source, real-time implementation of SemanticPaint, a system for geometric reconstruction, object-class segmentation and learning of 3D scenes. Using our system, a user can walk into a room wearing a depth camera and a virtual reality headset, and both densely reconstruct the 3D scene and interactively segment the environment into object classes such as 'chair', 'floor' and 'table'. The user interacts physically with the real-world scene, touching objects and using voice commands to assign them appropriate labels. These user-generated labels are leveraged by an online random forest-based machine learning algorithm, which is used to predict labels for previously unseen parts of the scene. The entire pipeline runs in real time, and the user stays 'in the loop' throughout the process, receiving immediate feedback about the progress of the labelling and interacting with the scene as necessary to refine the predicted segmentation.
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
Venue: arXiv cs.CL 2015
Year: 2015

This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging predictions of multiple models. The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset. Finally, we discuss our future plans using this corpus.
In-Network Neighborhood-Based Node Similarity Measure: A Unified Parametric Model
Venue: arXiv cs.SI 2015
Year: 2015

In many applications, we need to measure similarity between nodes in a large network based on features of their neighborhoods. Although in-network node similarity based on proximity has been well investigated, surprisingly, measuring in-network node similarity based on neighborhoods remains a largely untouched problem in literature. One grand challenge is that in different applications we may need different measurements that manifest different meanings of similarity. In this paper, we investigate the problem in a principled and systematic manner. We develop a unified parametric model and a series of four instance measures. Those instance similarity measures not only address a spectrum of various meanings of similarity, but also present a series of tradeoffs between computational cost and strictness of matching between neighborhoods of nodes being compared. By extensive experiments and case studies, we demonstrate the effectiveness of the proposed model and its instances.
Multiresolution Search of the Rigid Motion Space for Intensity Based Registration
Venue: arXiv cs.CV 2015
Year: 2015

We study the relation between the target functions of low-resolution and high-resolution intensity-based registration for the class of rigid transformations. Our results show that low resolution target values can tightly bound the high-resolution target function in natural images. This can help with analyzing and better understanding the process of multiresolution image registration. It also gives a guideline for designing multiresolution algorithms in which the search space in higher resolution registration is restricted given the fitness values for lower resolution image pairs. To demonstrate this, we incorporate our multiresolution technique into a Lipschitz global optimization framework. We show that using the multiresolution scheme can result in large gains in the efficiency of such algorithms. The method is evaluated by applying to 2D and 3D registration problems as well as the detection of reflective symmetry in 2D and 3D images.
Corpus COFLA: A research corpus for the Computational study of Flamenco Music
Venue: arXiv cs.SD 2015
Year: 2015

Flamenco is a music tradition from Southern Spain which attracts a growing community of enthusiasts around the world. Its unique melodic and rhythmic elements, the typically spontaneous and improvised interpretation and its diversity regarding styles make this still largely undocumented art form a particularly interesting material for musicological studies. In prior works it has already been demonstrated that research on computational analysis of flamenco music, despite it being a relatively new field, can provide powerful tools for the discovery and diffusion of this genre. In this paper we present corpusCOFLA, a data framework for the development of such computational tools. The proposed collection of audio recordings and meta-data serves as a pool for creating annotated subsets which can be used in development and evaluation of algorithms for specific music information retrieval tasks. First, we describe the design criteria for the corpus creation and then provide various examples of subsets drawn from the corpus. We showcase possible research applications in the context of computational study of flamenco music and give perspectives regarding further development of the corpus.
A Preliminary Study on the Learning Informativeness of Data Subsets
Venue: arXiv cs.CL 2015
Year: 2015

Estimating the internal state of a robotic system is complex: this is performed from multiple heterogeneous sensor inputs and knowledge sources. Discretization of such inputs is done to capture saliences, represented as symbolic information, which often presents structure and recurrence. As these sequences are used to reason over complex scenarios, a more compact representation would aid exactness of technical cognitive reasoning capabilities, which are today constrained by computational complexity issues and fallback to representational heuristics or human intervention. Such problems need to be addressed to ensure timely and meaningful human-robot interaction. Our work is towards understanding the variability of learning informativeness when training on subsets of a given input dataset. This is in view of reducing the training size while retaining the majority of the symbolic learning potential. We prove the concept on human-written texts, and conjecture this work will reduce training data size of sequential instructions, while preserving semantic relations, when gathering information from large remote sources.
Connected Big Data Measurement
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we have summarized how resilient Big Data monetization scheme outperforms state-of-the art schemes by maintaining a balance between CDS size and routing.
A synchronous rendering of hybrid systems for designing Plant-on-a-Chip (PoC)
Venue: arXiv cs.FL 2015
Year: 2015

Hybrid systems are discrete controllers that are used for controlling a physical process (plant) exhibiting continuous dynamics. A hybrid automata (HA) is a well known and widely used formal model for the specification of such systems. While many methods exist for simulating hybrid automata, there are no known approaches for the automatic code generation from HA that are semantic preserving. If this were feasible, it would enable the design of a plant-on-a-chip (PoC) system that could be used for the emulation of the plant to validate discrete controllers. Such an approach would need to be mathematically sound and should not rely on numerical solvers. We propose a method of PoC design for plant emulation, not possible before. The approach restricts input/output (I/O) HA models using a set of criteria for well-formedness which are statically verified. Following verification, we use an abstraction based on a synchronous approach to facilitate code generation. This is feasible through a sound transformation to synchronous HA. We compare our method (the developed tool called Piha) to the widely used Simulink R simulation framework and show that our method is superior in both execution time and code size. Our approach to the PoC problem paves the way for the emulation of physical plants in diverse domains such as robotics, automation, medical devices, and intelligent transportation systems.
Processing Regular Path Queries on Arbitrarily Distributed Data
Venue: arXiv cs.DC 2015
Year: 2015

Regular Path Queries (RPQs) are a type of graph query where answers are pairs of nodes connected by a sequence of edges matching a regular expression. We study the techniques to process such queries on a distributed graph of data. While many techniques assume the location of each data element (node or edge) is known, when the components of the distributed system are autonomous, the data will be arbitrarily distributed. As the different query processing strategies are equivalently costly in the worst case, we isolate query-dependent cost factors and present a method to choose between strategies, using new query cost estimation techniques. We evaluate our techniques using meaningful queries on biomedical data.
Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
Venue: arXiv cs.CL 2015
Year: 2015

Text alignment and text quality are critical to the accuracy of Machine Translation (MT) systems, some NLP tools, and any other text processing tasks requiring bilingual data. This research proposes a language independent bi-sentence filtering approach based on Polish (not a position-sensitive language) to English experiments. This cleaning approach was developed on the TED Talks corpus and also initially tested on the Wikipedia comparable corpus, but it can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence comparison. Some of them leverage synonyms and semantic and structural analysis of text as additional information. Minimization of data loss was ensured. An improvement in MT system score with text processed using the tool is discussed.
Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes
Venue: arXiv cs.CV 2015
Year: 2015

We consider the problem of matching two shapes assuming these shapes are related by an elastic deformation. Using linearized elasticity theory and the finite element method we seek an elastic deformation that is caused by simple external boundary forces and accounts for the difference between the two shapes. Our main contribution is in proposing a cost function and an optimization procedure to minimize the symmetric difference between the deformed and the target shapes as an alternative to point matches that guide the matching in other techniques. We show how to approximate the nonlinear optimization problem by a sequence of convex problems. We demonstrate the utility of our method in experiments and compare it to an ICP-like matching algorithm.
A Fully-Unrolled LDPC Decoder Based on Quantized Message Passing
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose a finite alphabet message passing algorithm for LDPC codes that replaces the standard min-sum variable node update rule by a mapping based on generic look-up tables. This mapping is designed in a way that maximizes the mutual information between the decoder messages and the codeword bits. We show that our decoder can deliver the same error rate performance as the conventional decoder with a much smaller message bit-width. Finally, we use the proposed algorithm to design a fully unrolled LDPC decoder hardware architecture.
Delaunay Triangulations of Degenerate Point Sets
Venue: arXiv cs.CG 2015
Year: 2015

The Delaunay triangulation (DT) is one of the most common and useful triangulations of point sets $P$ in the plane. DT is not unique when $P$ is degenerate, specifically when it contains quadruples of co-circular points. One way to achieve uniqueness is by applying a small (or infinitesimal) perturbation to $P$. We consider a specific perturbation of such degenerate sets, in which the coordinates of each point are independently perturbed by normally distributed small quantities, and investigate the effect of such perturbations on the DT of the set. We focus on two special configurations, where (1) the points of $P$ form a uniform grid, and (2) the points of $P$ are vertices of a regular polygon. We present interesting (and sometimes surprising) empirical findings and properties of the perturbed DTs for these cases, and give theoretical explanations to some of them.
A Type-Theoretic Approach to Resolution
Venue: arXiv cs.LO 2015
Year: 2015

We propose a new type-theoretic approach to SLD-resolution and Horn-clause logic programming. It views Horn formulas as types, and derivations for a given query as a construction of the inhabitant (a proof-term) for the type given by the query. We propose a method of program transformation that allows to transform logic programs in such a way that proof evidence is computed alongside SLD-derivations. We discuss two applications of this approach: in recently proposed productivity theory of structural resolution, and in type class inference.
The H-index Paradox: Your Coauthors Have a Higher H-index than You Do
Venue: arXiv cs.SI 2015
Year: 2015

One interesting phenomenon that emerges from the typical structure of social networks is the friendship paradox. It states that your friends have on average more friends than you do. Recent efforts have explored variations of it, with numerous implications for the dynamics of social networks. However, the friendship paradox and its variations consider only the topological structure of the networks and neglect many other characteristics that are correlated with node degree. In this article, we take the case of scientific collaborations to investigate whether a similar paradox also arises in terms of a researcher's scientific productivity as measured by her H-index. The H-index is a widely used metric in academia to capture both the quality and the quantity of a researcher's scientific output. It is likely that a researcher may use her coauthors' H-indexes as a way to infer whether her own H-index is adequate in her research area. Nevertheless, in this article, we show that the average H-index of a researcher's coauthors is usually higher than her own H-index. We present empirical evidence of this paradox and discuss some of its potential consequences.
Scalable MCMC for Mixed Membership Stochastic Blockmodels
Venue: arXiv cs.LG 2015
Year: 2015

We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current state-of-the-art algorithm based on stochastic variational inference. In addition we develop an approximation that can handle models that entertain a very large number of communities. The experimental results show that SG-MCMC strictly dominates competing algorithms in all cases.
Error-Correcting Functional Index Codes, Generalized Exclusive Laws and Graph Coloring
Venue: arXiv cs.IT 2015
Year: 2015

We consider the \emph{functional index coding problem} over an error-free broadcast network in which a source generates a set of messages and there are multiple receivers, each holding a set of functions of source messages in its cache, called the \emph{Has-set}, and demands to know another set of functions of messages, called the \emph{Want-set}. Cognizant of the receivers' \emph{Has-sets}, the source aims to satisfy the demands of each receiver by making coded transmissions, called a \emph{functional index code}. The objective is to minimize the number of such transmissions required. The restriction a receiver's demands pose on the code is represented via a constraint called the \emph{generalized exclusive law} and obtain a code using the \emph{confusion graph} constructed using these constraints. Bounds on the size of an optimal code based on the parameters of the confusion graph are presented. Next, we consider the case of erroneous transmissions and provide a necessary and sufficient condition that an FIC must satisfy for correct decoding of desired functions at each receiver and obtain a lower bound on the length of an error-correcting FIC.
Evaluating the Competency of a First-Order Ontology
Venue: arXiv cs.AI 2015
Year: 2015

We report on the results of evaluating the competency of a first-order ontology for its use with automated theorem provers (ATPs). The evaluation follows the adaptation of the methodology based on competency questions (CQs) [Grüninger&Fox,1995] to the framework of first-order logic, which is presented in [Álvez&Lucio&Rigau,2015], and is applied to Adimen-SUMO [Álvez&Lucio&Rigau,2015]. The set of CQs used for this evaluation has been automatically generated from a small set of semantic patterns and the mapping of WordNet to SUMO. Analysing the results, we can conclude that it is feasible to use ATPs for working with Adimen-SUMO v2.4, enabling the resolution of goals by means of performing non-trivial inferences.
Holographic Embeddings of Knowledge Graphs
Venue: arXiv cs.AI 2015
Year: 2015

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.
The Physical Web in Smart Cities
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we discuss the physical web projects based on network proximity for Smart Cities. In general, the Physical Web is an approach for connecting any physical object to the web. With this approach, we can navigate and control physical objects in the world surrounding mobile devices. Alternatively, we can execute services on mobile devices, depending on the surrounding physical objects. Technically, there are different ways to enumerate physical objects. In this paper, we will target the models based on the wireless proximity.
Lamassu: Storage-Efficient Host-Side Encryption
Venue: arXiv cs.CR 2015
Year: 2015

Many storage customers are adopting encryption solutions to protect critical data. Most existing encryption solutions sit in, or near, the application that is the source of critical data, upstream of the primary storage system. Placing encryption near the source ensures that data remains encrypted throughout the storage stack, making it easier to use untrusted storage, such as public clouds. Unfortunately, such a strategy also prevents downstream storage systems from applying content-based features, such as deduplication, to the data. In this paper, we present Lamassu, an encryption solution that uses block-oriented, host-based, convergent encryption to secure data, while preserving storage-based data deduplication. Unlike past convergent encryption systems, which typically store encryption metadata in a dedicated store, our system transparently inserts its metadata into each file's data stream. This allows us to add Lamassu to an application stack without modifying either the client application or the storage controller. In this paper, we lay out the architecture and security model used in our system, and present a new model for maintaining metadata consistency and data integrity in a convergent encryption environment. We also evaluate its storage efficiency and I/O performance by using a variety of microbenchmarks, showing that Lamassu provides excellent storage efficiency, while achieving I/O throughput on par with similar conventional encryption systems.
A cost function for similarity-based hierarchical clustering
Venue: arXiv cs.DS 2015
Year: 2015

The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction procedure with a provably good approximation ratio.
Applying Operating System Principles to SDN Controller Design
Venue: arXiv cs.NI 2015
Year: 2015

Rather than creating yet another network controller which provides a framework in a specific (potentially new) programming language and runs as a monolithic application, in this paper we extend an existing operating system and leverage its software ecosystem in order to serve as a practical SDN controller. This paper introduces yanc, a controller platform for software-defined networks which exposes the network configuration and state as a file system, enabling user and system applications to interact through standard file I/O, and to easily take advantage of the tools available on the host operating system. In yanc, network applications are separate processes, are provided by multiple sources, and may be written in any language. Applications benefit from common and powerful technologies such as the virtual file system (VFS) layer, which we leverage to layer a distributed file system on top of, and Linux namespaces, which we use to isolate applications with different views (e.g., slices). In this paper we present the goals and design of yanc. Our initial prototype is built with the FUSE file system in user space on Linux and has been demonstrated with a simple static flow pusher application. Effectively, we are making Linux the network operating system.
A Makespan Lower Bound for the Scheduling of the Tiled Cholesky Factorization based on ALAP scheduling
Venue: arXiv cs.DC 2015
Year: 2015

Due to the advent of multicore architectures and massive parallelism, the tiled Cholesky factorization algorithm has recently received plenty of attention and is often referenced by practitioners as a case study. It is also implemented in mainstream dense linear algebra libraries. However, we note that theoretical study of the parallelism of this algorithm is currently lacking. In this paper, we present new theoretical results about the tiled Cholesky factorization in the context of a parallel homogeneous model without communication costs. We use standard flop-based weights for the tasks. For a $t$-by-$t$ matrix, we know that the critical path of the tiled Cholesky algorithm is $9t-10$ and that the weight of all tasks is $t^3$. In this context, we prove that no schedule with less than $0.185 t^2$ processing units can finish in a time less than the critical path. In perspective, a naive bound gives $0.11 t^2.$ We then give a schedule which needs less than $0.25 t^2+0.16t+3$ processing units to complete in the time of the critical path. In perspective, a naive schedule gives $0.50 t^2.$ In addition, given a fixed number of processing units, $p$, we give a lower bound on the execution time as follows: $$\max( \frac{t^{3}}{p}, \frac{t^{3}}{p} - 3\frac{t^2}{p} + 6\sqrt{2p} - 7 , 9t-10).$$ The interest of the latter formula lies in the middle term. Our results stem from the observation that the tiled Cholesky factorization is much better behaved when we schedule it with an ALAP (As Late As Possible) heuristic than an ASAP (As Soon As Possible) heuristic. We also provide scheduling heuristics which match closely the lower bound on execution time. We believe that our theoretical results will help practical scheduling studies. Indeed, our results enable to better characterize the quality of a practical schedule with respect to an optimal schedule.